The value of big data risk control needs no further introduction. This has become the core competitiveness of mutual finance companies and an important feature that distinguishes mutual finance from traditional finance. Mutual financial companies can provide inclusive financial services to people who cannot be served by traditional financial institutions, and risk control methods based on big data play an important role. By comprehensively collecting various user data information and conducting effective modeling and iteration to evaluate the user's credit status, we can decide whether to lend, the loan amount, and the loan interest rate.

Compared with the traditional financial risk control model, big data risk control can complete the review of loan applications for a large number of users through large-scale data calculations by machines, improving work efficiency. Traditional financial review is completed manually, and the efficiency will be relatively limited; big data risk control can quickly iterate based on new situations and new data that appear in business operations, enhancing the effectiveness of the model; machines and software can work in "24*365" mode , get rid of the constraints of working hours.

Mutual financial companies are focusing on big data risk control

Currently, many mutual financial companies are doing big data risk control. Huxiu’s previous article took stock of BAT’s consumer finance business. Summarized BAT’s technology in big data risk control. JD.com also has related layouts.

BATJ’s big data risk control technology

In addition to large companies like BATJ, new mutual financial companies that have emerged in recent years and have reached a certain scale have also vigorously deployed big data risk control in order to Mainly online loan companies and loan search platforms, most of which have launched relevant big data risk control technology systems.

Big data risk control technology of some domestic online lending companies and financial search companies

In addition, many financial technology companies have emerged that develop big data risk control technology. Develop big data anti-fraud models and credit assessment models, and export technology to financial companies with resources on the capital or asset side. Such companies are also increasingly favored by capital. Statistics show that in the past four months, at least 8 companies that export big data risk control technology have received financing, among which 9th Power Big Data, 51 Credit Card, and Yongqianbao have all raised Series B or above.

From large companies like BAT to start-up companies in the mutual finance field, they are all working hard to develop big data risk control technology. The value of big data risk control is evident.

What exactly is big data risk control?

The construction of a big data risk control model includes clarifying model goals, defining target variables, determining samples, determining analysis techniques, building models, preliminary verification of models, data processing, model iteration, etc. After exclusive interviews with Yongqianbao CEO Jiao Ke and other industry insiders, we found that the core work includes three aspects, namely obtaining data, building models, optimizing the models in practice, and iterating.

Sources of data

For big data risk control business, data sources mainly include several parts:

First, the data information submitted by users when applying; Such as age, gender, place of origin, income status, etc. These data can understand the user's basic situation and verify the user's identity;

The second is the behavioral data generated by the user during use, including changes in information, selections, etc. The order of filling in information, the equipment used in the application, etc. can be used for feature mining through user behavior;

The third is the transaction data accumulated by users on the platform. If the company has been operating for a long time, it can be accumulated and compared. There is a large amount of data related to user borrowings. This type of data will be of high value for judging user credit;

The fourth is third-party data, including data from governments, public utilities, banks and other institutions, as well as users’ Data retained on Internet applications such as e-commerce, social networks, and online news. This type of data can display user characteristics from multiple perspectives. Using this data for modeling analysis, the correlation between different characteristics and credit levels can be found.